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Erschienen in:
Study of the Effect of Magnetic Field and Pulsating Flow on the Thermoelectric Cooler Performance Using Fuzzy Logic Control Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Supervised Classification Techniques for Identifying Alzheimer’s Disease

verfasst von : Yasmeen Farouk, Sherine Rady

Erschienen in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018

Verlag: Springer International Publishing

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Abstract

Alzheimer’s Disease is a serious form of dementia. With no current cure, treatments focus on slowing the progression of the disease and controlling its symptoms. Early diagnosis by studying the biomarkers found in structural MRI is the key. This paper proposes a method which combines texture features extracted from gray level co-occurrence matrix and voxel-based morphometry neuroimaging analysis to classify Alzheimer’s disease patients. Different supervised classification techniques are studied, support vector machine, k-nearest neighbor, and decision tree, to obtain best identification accuracy. The paper explores as well the discriminative power for Alzheimer’s disease of certain anatomical regions of interest. The proposed technique is applied on gray matter tissues, and managed successfully to differentiate between Alzheimer’s disease patients and normal controls with accuracy 92%.

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Vorheriges Kapitel Multi-class Support Vector Machine Training and Classification Based on MPI-GPU Hybrid Parallel Architecture
Nächstes Kapitel Supervised Classification of Cancers Based on Copy Number Variation
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Metadaten
Titel
Supervised Classification Techniques for Identifying Alzheimer’s Disease
verfasst von
Yasmeen Farouk
Sherine Rady
Copyright-Jahr
2019
Buch
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018

Print ISBN: 978-3-319-99009-5

Electronic ISBN: 978-3-319-99010-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-99010-1_17